Overview

Dataset statistics

Number of variables20
Number of observations372
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory69.1 KiB
Average record size in memory190.3 B

Variable types

Numeric10
Categorical8
Boolean2

Alerts

city has a high cardinality: 368 distinct valuesHigh cardinality
payment_due is highly overall correlated with gender and 8 other fieldsHigh correlation
prescription_cost is highly overall correlated with gender and 8 other fieldsHigh correlation
gender is highly overall correlated with payment_due and 1 other fieldsHigh correlation
state is highly overall correlated with payment_due and 1 other fieldsHigh correlation
has_insurance is highly overall correlated with payment_due and 1 other fieldsHigh correlation
visited_last_month is highly overall correlated with payment_due and 1 other fieldsHigh correlation
payment_method is highly overall correlated with payment_due and 1 other fieldsHigh correlation
preferred_doctor is highly overall correlated with payment_due and 1 other fieldsHigh correlation
disease_diagnosed is highly overall correlated with payment_due and 1 other fieldsHigh correlation
medication_prescribed is highly overall correlated with payment_due and 1 other fieldsHigh correlation
type_of_appointment is highly overall correlated with payment_due and 1 other fieldsHigh correlation
city is uniformly distributedUniform
payment_due has unique valuesUnique
prescription_cost has unique valuesUnique

Reproduction

Analysis started2023-09-21 02:36:55.161730
Analysis finished2023-09-21 02:39:38.818434
Duration2 minutes and 43.66 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

patient_age
Real number (ℝ)

Distinct60
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.44086
Minimum18
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-09-21T02:39:38.994024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21.55
Q136
median45
Q354.25
95-th percentile68
Maximum90
Range72
Interquartile range (IQR)18.25

Descriptive statistics

Standard deviation13.813515
Coefficient of variation (CV)0.30398886
Kurtosis-0.0080718374
Mean45.44086
Median Absolute Deviation (MAD)9
Skewness0.10710029
Sum16904
Variance190.8132
MonotonicityNot monotonic
2023-09-21T02:39:39.305393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 20
 
5.4%
18 14
 
3.8%
42 14
 
3.8%
50 13
 
3.5%
43 13
 
3.5%
38 12
 
3.2%
40 12
 
3.2%
53 11
 
3.0%
58 10
 
2.7%
39 10
 
2.7%
Other values (50) 243
65.3%
ValueCountFrequency (%)
18 14
3.8%
19 2
 
0.5%
20 2
 
0.5%
21 1
 
0.3%
22 2
 
0.5%
23 4
 
1.1%
24 6
1.6%
25 2
 
0.5%
26 3
 
0.8%
27 7
1.9%
ValueCountFrequency (%)
90 1
 
0.3%
88 1
 
0.3%
86 2
 
0.5%
78 1
 
0.3%
76 1
 
0.3%
73 1
 
0.3%
72 2
 
0.5%
71 3
0.8%
69 5
1.3%
68 3
0.8%

gender
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Female
138 
Other
124 
Male
110 

Length

Max length6
Median length5
Mean length5.0752688
Min length4

Characters and Unicode

Total characters1888
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 138
37.1%
Other 124
33.3%
Male 110
29.6%

Length

2023-09-21T02:39:39.639636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T02:39:39.940235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 138
37.1%
other 124
33.3%
male 110
29.6%

Most occurring characters

ValueCountFrequency (%)
e 510
27.0%
a 248
13.1%
l 248
13.1%
F 138
 
7.3%
m 138
 
7.3%
O 124
 
6.6%
t 124
 
6.6%
h 124
 
6.6%
r 124
 
6.6%
M 110
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1516
80.3%
Uppercase Letter 372
 
19.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 510
33.6%
a 248
16.4%
l 248
16.4%
m 138
 
9.1%
t 124
 
8.2%
h 124
 
8.2%
r 124
 
8.2%
Uppercase Letter
ValueCountFrequency (%)
F 138
37.1%
O 124
33.3%
M 110
29.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 1888
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 510
27.0%
a 248
13.1%
l 248
13.1%
F 138
 
7.3%
m 138
 
7.3%
O 124
 
6.6%
t 124
 
6.6%
h 124
 
6.6%
r 124
 
6.6%
M 110
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 510
27.0%
a 248
13.1%
l 248
13.1%
F 138
 
7.3%
m 138
 
7.3%
O 124
 
6.6%
t 124
 
6.6%
h 124
 
6.6%
r 124
 
6.6%
M 110
 
5.8%

city
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct368
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Port John
 
2
Port Jennifer
 
2
North Kelly
 
2
Smithchester
 
2
New Loganburgh
 
1
Other values (363)
363 

Length

Max length20
Median length17
Mean length12.180108
Min length6

Characters and Unicode

Total characters4531
Distinct characters49
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique364 ?
Unique (%)97.8%

Sample

1st rowNorth Julieland
2nd rowEmmatown
3rd rowSouth Melissa
4th rowHamptontown
5th rowNorth Sierraview

Common Values

ValueCountFrequency (%)
Port John 2
 
0.5%
Port Jennifer 2
 
0.5%
North Kelly 2
 
0.5%
Smithchester 2
 
0.5%
New Loganburgh 1
 
0.3%
Bryanchester 1
 
0.3%
Douglasfort 1
 
0.3%
West Allison 1
 
0.3%
East Jamesport 1
 
0.3%
West William 1
 
0.3%
Other values (358) 358
96.2%

Length

2023-09-21T02:39:40.224865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east 31
 
5.5%
south 30
 
5.3%
port 26
 
4.6%
lake 26
 
4.6%
west 26
 
4.6%
new 26
 
4.6%
north 24
 
4.3%
john 6
 
1.1%
johnshire 2
 
0.4%
richard 2
 
0.4%
Other values (347) 362
64.5%

Most occurring characters

ValueCountFrequency (%)
e 419
 
9.2%
t 377
 
8.3%
a 369
 
8.1%
r 363
 
8.0%
o 297
 
6.6%
n 257
 
5.7%
h 250
 
5.5%
s 229
 
5.1%
i 226
 
5.0%
189
 
4.2%
Other values (39) 1555
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3781
83.4%
Uppercase Letter 561
 
12.4%
Space Separator 189
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 419
11.1%
t 377
10.0%
a 369
9.8%
r 363
9.6%
o 297
 
7.9%
n 257
 
6.8%
h 250
 
6.6%
s 229
 
6.1%
i 226
 
6.0%
u 156
 
4.1%
Other values (16) 838
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 67
11.9%
N 60
10.7%
J 54
9.6%
W 50
8.9%
E 47
 
8.4%
L 39
 
7.0%
P 37
 
6.6%
M 36
 
6.4%
C 25
 
4.5%
A 23
 
4.1%
Other values (12) 123
21.9%
Space Separator
ValueCountFrequency (%)
189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4342
95.8%
Common 189
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 419
 
9.6%
t 377
 
8.7%
a 369
 
8.5%
r 363
 
8.4%
o 297
 
6.8%
n 257
 
5.9%
h 250
 
5.8%
s 229
 
5.3%
i 226
 
5.2%
u 156
 
3.6%
Other values (38) 1399
32.2%
Common
ValueCountFrequency (%)
189
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4531
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 419
 
9.2%
t 377
 
8.3%
a 369
 
8.1%
r 363
 
8.0%
o 297
 
6.6%
n 257
 
5.7%
h 250
 
5.5%
s 229
 
5.1%
i 226
 
5.0%
189
 
4.2%
Other values (39) 1555
34.3%

state
Categorical

Distinct50
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Vermont
 
16
Connecticut
 
15
Texas
 
14
Pennsylvania
 
12
Indiana
 
11
Other values (45)
304 

Length

Max length14
Median length12
Mean length8.4596774
Min length4

Characters and Unicode

Total characters3147
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVermont
2nd rowConnecticut
3rd rowOregon
4th rowNorth Dakota
5th rowMaryland

Common Values

ValueCountFrequency (%)
Vermont 16
 
4.3%
Connecticut 15
 
4.0%
Texas 14
 
3.8%
Pennsylvania 12
 
3.2%
Indiana 11
 
3.0%
Arizona 11
 
3.0%
Kansas 10
 
2.7%
North Dakota 10
 
2.7%
Colorado 10
 
2.7%
South Dakota 10
 
2.7%
Other values (40) 253
68.0%

Length

2023-09-21T02:39:40.511793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 23
 
5.2%
dakota 20
 
4.5%
north 19
 
4.3%
south 18
 
4.0%
carolina 17
 
3.8%
vermont 16
 
3.6%
connecticut 15
 
3.4%
texas 14
 
3.1%
virginia 14
 
3.1%
pennsylvania 12
 
2.7%
Other values (42) 277
62.2%

Most occurring characters

ValueCountFrequency (%)
a 424
13.5%
i 278
 
8.8%
n 278
 
8.8%
o 260
 
8.3%
s 205
 
6.5%
e 203
 
6.5%
r 168
 
5.3%
t 147
 
4.7%
l 97
 
3.1%
h 89
 
2.8%
Other values (36) 998
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2629
83.5%
Uppercase Letter 445
 
14.1%
Space Separator 73
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 424
16.1%
i 278
10.6%
n 278
10.6%
o 260
9.9%
s 205
7.8%
e 203
7.7%
r 168
 
6.4%
t 147
 
5.6%
l 97
 
3.7%
h 89
 
3.4%
Other values (14) 480
18.3%
Uppercase Letter
ValueCountFrequency (%)
M 60
13.5%
N 56
12.6%
C 49
11.0%
I 34
 
7.6%
V 30
 
6.7%
A 29
 
6.5%
D 25
 
5.6%
O 22
 
4.9%
W 21
 
4.7%
T 19
 
4.3%
Other values (11) 100
22.5%
Space Separator
ValueCountFrequency (%)
73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3074
97.7%
Common 73
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 424
13.8%
i 278
 
9.0%
n 278
 
9.0%
o 260
 
8.5%
s 205
 
6.7%
e 203
 
6.6%
r 168
 
5.5%
t 147
 
4.8%
l 97
 
3.2%
h 89
 
2.9%
Other values (35) 925
30.1%
Common
ValueCountFrequency (%)
73
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 424
13.5%
i 278
 
8.8%
n 278
 
8.8%
o 260
 
8.3%
s 205
 
6.5%
e 203
 
6.5%
r 168
 
5.3%
t 147
 
4.7%
l 97
 
3.1%
h 89
 
2.8%
Other values (36) 998
31.7%
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
True
186 
False
186 
ValueCountFrequency (%)
True 186
50.0%
False 186
50.0%
2023-09-21T02:39:40.795269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
True
203 
False
169 
ValueCountFrequency (%)
True 203
54.6%
False 169
45.4%
2023-09-21T02:39:41.038582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

payment_method
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Insurance
128 
Cash
127 
Card
117 

Length

Max length9
Median length4
Mean length5.7204301
Min length4

Characters and Unicode

Total characters2128
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCash
2nd rowCard
3rd rowCash
4th rowCard
5th rowInsurance

Common Values

ValueCountFrequency (%)
Insurance 128
34.4%
Cash 127
34.1%
Card 117
31.5%

Length

2023-09-21T02:39:41.265254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T02:39:41.540833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
insurance 128
34.4%
cash 127
34.1%
card 117
31.5%

Most occurring characters

ValueCountFrequency (%)
a 372
17.5%
n 256
12.0%
s 255
12.0%
r 245
11.5%
C 244
11.5%
I 128
 
6.0%
u 128
 
6.0%
c 128
 
6.0%
e 128
 
6.0%
h 127
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1756
82.5%
Uppercase Letter 372
 
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 372
21.2%
n 256
14.6%
s 255
14.5%
r 245
14.0%
u 128
 
7.3%
c 128
 
7.3%
e 128
 
7.3%
h 127
 
7.2%
d 117
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
C 244
65.6%
I 128
34.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2128
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 372
17.5%
n 256
12.0%
s 255
12.0%
r 245
11.5%
C 244
11.5%
I 128
 
6.0%
u 128
 
6.0%
c 128
 
6.0%
e 128
 
6.0%
h 127
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 372
17.5%
n 256
12.0%
s 255
12.0%
r 245
11.5%
C 244
11.5%
I 128
 
6.0%
u 128
 
6.0%
c 128
 
6.0%
e 128
 
6.0%
h 127
 
6.0%

preferred_doctor
Categorical

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Dr. Williams
84 
Dr. Brown
84 
Dr. Smith
74 
Dr. Jones
70 
Dr. Johnson
60 

Length

Max length12
Median length9
Mean length10
Min length9

Characters and Unicode

Total characters3720
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDr. Johnson
2nd rowDr. Williams
3rd rowDr. Brown
4th rowDr. Brown
5th rowDr. Brown

Common Values

ValueCountFrequency (%)
Dr. Williams 84
22.6%
Dr. Brown 84
22.6%
Dr. Smith 74
19.9%
Dr. Jones 70
18.8%
Dr. Johnson 60
16.1%

Length

2023-09-21T02:39:41.788890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T02:39:42.093317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
dr 372
50.0%
williams 84
 
11.3%
brown 84
 
11.3%
smith 74
 
9.9%
jones 70
 
9.4%
johnson 60
 
8.1%

Most occurring characters

ValueCountFrequency (%)
r 456
12.3%
D 372
10.0%
. 372
10.0%
372
10.0%
n 274
 
7.4%
o 274
 
7.4%
i 242
 
6.5%
s 214
 
5.8%
l 168
 
4.5%
m 158
 
4.2%
Other values (9) 818
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2232
60.0%
Uppercase Letter 744
 
20.0%
Other Punctuation 372
 
10.0%
Space Separator 372
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 456
20.4%
n 274
12.3%
o 274
12.3%
i 242
10.8%
s 214
9.6%
l 168
 
7.5%
m 158
 
7.1%
h 134
 
6.0%
a 84
 
3.8%
w 84
 
3.8%
Other values (2) 144
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
D 372
50.0%
J 130
 
17.5%
B 84
 
11.3%
W 84
 
11.3%
S 74
 
9.9%
Other Punctuation
ValueCountFrequency (%)
. 372
100.0%
Space Separator
ValueCountFrequency (%)
372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2976
80.0%
Common 744
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 456
15.3%
D 372
12.5%
n 274
9.2%
o 274
9.2%
i 242
8.1%
s 214
 
7.2%
l 168
 
5.6%
m 158
 
5.3%
h 134
 
4.5%
J 130
 
4.4%
Other values (7) 554
18.6%
Common
ValueCountFrequency (%)
. 372
50.0%
372
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 456
12.3%
D 372
10.0%
. 372
10.0%
372
10.0%
n 274
 
7.4%
o 274
 
7.4%
i 242
 
6.5%
s 214
 
5.8%
l 168
 
4.5%
m 158
 
4.2%
Other values (9) 818
22.0%
Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Flu
82 
None
77 
Allergy
74 
Covid-19
71 
Cold
68 

Length

Max length8
Median length7
Mean length5.1397849
Min length3

Characters and Unicode

Total characters1912
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowFlu
3rd rowCovid-19
4th rowCold
5th rowCovid-19

Common Values

ValueCountFrequency (%)
Flu 82
22.0%
None 77
20.7%
Allergy 74
19.9%
Covid-19 71
19.1%
Cold 68
18.3%

Length

2023-09-21T02:39:42.377979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T02:39:42.664221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
flu 82
22.0%
none 77
20.7%
allergy 74
19.9%
covid-19 71
19.1%
cold 68
18.3%

Most occurring characters

ValueCountFrequency (%)
l 298
15.6%
o 216
 
11.3%
e 151
 
7.9%
d 139
 
7.3%
C 139
 
7.3%
F 82
 
4.3%
u 82
 
4.3%
N 77
 
4.0%
n 77
 
4.0%
y 74
 
3.9%
Other values (8) 577
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1327
69.4%
Uppercase Letter 372
 
19.5%
Decimal Number 142
 
7.4%
Dash Punctuation 71
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 298
22.5%
o 216
16.3%
e 151
11.4%
d 139
10.5%
u 82
 
6.2%
n 77
 
5.8%
y 74
 
5.6%
g 74
 
5.6%
r 74
 
5.6%
v 71
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
C 139
37.4%
F 82
22.0%
N 77
20.7%
A 74
19.9%
Decimal Number
ValueCountFrequency (%)
1 71
50.0%
9 71
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1699
88.9%
Common 213
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 298
17.5%
o 216
12.7%
e 151
8.9%
d 139
 
8.2%
C 139
 
8.2%
F 82
 
4.8%
u 82
 
4.8%
N 77
 
4.5%
n 77
 
4.5%
y 74
 
4.4%
Other values (5) 364
21.4%
Common
ValueCountFrequency (%)
- 71
33.3%
1 71
33.3%
9 71
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 298
15.6%
o 216
 
11.3%
e 151
 
7.9%
d 139
 
7.3%
C 139
 
7.3%
F 82
 
4.3%
u 82
 
4.3%
N 77
 
4.0%
n 77
 
4.0%
y 74
 
3.9%
Other values (8) 577
30.2%
Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Med_B
91 
Med_A
76 
Med_E
75 
Med_C
68 
Med_D
62 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1860
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMed_B
2nd rowMed_A
3rd rowMed_B
4th rowMed_C
5th rowMed_D

Common Values

ValueCountFrequency (%)
Med_B 91
24.5%
Med_A 76
20.4%
Med_E 75
20.2%
Med_C 68
18.3%
Med_D 62
16.7%

Length

2023-09-21T02:39:42.937832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T02:39:43.215584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
med_b 91
24.5%
med_a 76
20.4%
med_e 75
20.2%
med_c 68
18.3%
med_d 62
16.7%

Most occurring characters

ValueCountFrequency (%)
M 372
20.0%
e 372
20.0%
d 372
20.0%
_ 372
20.0%
B 91
 
4.9%
A 76
 
4.1%
E 75
 
4.0%
C 68
 
3.7%
D 62
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 744
40.0%
Lowercase Letter 744
40.0%
Connector Punctuation 372
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 372
50.0%
B 91
 
12.2%
A 76
 
10.2%
E 75
 
10.1%
C 68
 
9.1%
D 62
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
e 372
50.0%
d 372
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1488
80.0%
Common 372
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 372
25.0%
e 372
25.0%
d 372
25.0%
B 91
 
6.1%
A 76
 
5.1%
E 75
 
5.0%
C 68
 
4.6%
D 62
 
4.2%
Common
ValueCountFrequency (%)
_ 372
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 372
20.0%
e 372
20.0%
d 372
20.0%
_ 372
20.0%
B 91
 
4.9%
A 76
 
4.1%
E 75
 
4.0%
C 68
 
3.7%
D 62
 
3.3%
Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Follow-Up
101 
Emergency
99 
General
95 
Specialist
77 

Length

Max length10
Median length9
Mean length8.6962366
Min length7

Characters and Unicode

Total characters3235
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmergency
2nd rowEmergency
3rd rowGeneral
4th rowFollow-Up
5th rowFollow-Up

Common Values

ValueCountFrequency (%)
Follow-Up 101
27.2%
Emergency 99
26.6%
General 95
25.5%
Specialist 77
20.7%

Length

2023-09-21T02:39:43.476115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T02:39:43.773359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
follow-up 101
27.2%
emergency 99
26.6%
general 95
25.5%
specialist 77
20.7%

Most occurring characters

ValueCountFrequency (%)
e 465
14.4%
l 374
 
11.6%
o 202
 
6.2%
r 194
 
6.0%
n 194
 
6.0%
p 178
 
5.5%
c 176
 
5.4%
a 172
 
5.3%
i 154
 
4.8%
F 101
 
3.1%
Other values (11) 1025
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2661
82.3%
Uppercase Letter 473
 
14.6%
Dash Punctuation 101
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 465
17.5%
l 374
14.1%
o 202
7.6%
r 194
7.3%
n 194
7.3%
p 178
 
6.7%
c 176
 
6.6%
a 172
 
6.5%
i 154
 
5.8%
w 101
 
3.8%
Other values (5) 451
16.9%
Uppercase Letter
ValueCountFrequency (%)
F 101
21.4%
U 101
21.4%
E 99
20.9%
G 95
20.1%
S 77
16.3%
Dash Punctuation
ValueCountFrequency (%)
- 101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3134
96.9%
Common 101
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 465
14.8%
l 374
11.9%
o 202
 
6.4%
r 194
 
6.2%
n 194
 
6.2%
p 178
 
5.7%
c 176
 
5.6%
a 172
 
5.5%
i 154
 
4.9%
F 101
 
3.2%
Other values (10) 924
29.5%
Common
ValueCountFrequency (%)
- 101
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3235
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 465
14.4%
l 374
 
11.6%
o 202
 
6.2%
r 194
 
6.0%
n 194
 
6.0%
p 178
 
5.5%
c 176
 
5.4%
a 172
 
5.3%
i 154
 
4.8%
F 101
 
3.1%
Other values (11) 1025
31.7%

average_hr
Real number (ℝ)

Distinct51
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.392473
Minimum53
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-09-21T02:39:44.031392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile65
Q172
median80
Q386
95-th percentile96
Maximum109
Range56
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.8826946
Coefficient of variation (CV)0.12447899
Kurtosis-0.24845476
Mean79.392473
Median Absolute Deviation (MAD)7
Skewness0.1414715
Sum29534
Variance97.667652
MonotonicityNot monotonic
2023-09-21T02:39:44.314710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 19
 
5.1%
80 17
 
4.6%
68 16
 
4.3%
82 16
 
4.3%
73 14
 
3.8%
81 14
 
3.8%
78 14
 
3.8%
79 14
 
3.8%
75 14
 
3.8%
85 14
 
3.8%
Other values (41) 220
59.1%
ValueCountFrequency (%)
53 1
 
0.3%
55 1
 
0.3%
56 2
0.5%
57 1
 
0.3%
59 1
 
0.3%
60 1
 
0.3%
61 1
 
0.3%
62 1
 
0.3%
63 3
0.8%
64 2
0.5%
ValueCountFrequency (%)
109 1
 
0.3%
108 1
 
0.3%
105 1
 
0.3%
103 1
 
0.3%
102 1
 
0.3%
100 2
 
0.5%
99 2
 
0.5%
98 4
1.1%
97 3
 
0.8%
96 8
2.2%

average_bp
Real number (ℝ)

Distinct56
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.50538
Minimum80
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-09-21T02:39:44.619895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile84
Q198
median111
Q3120
95-th percentile133
Maximum140
Range60
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.523611
Coefficient of variation (CV)0.13262921
Kurtosis-0.56650491
Mean109.50538
Median Absolute Deviation (MAD)10
Skewness-0.071494313
Sum40736
Variance210.93528
MonotonicityNot monotonic
2023-09-21T02:39:44.908199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 14
 
3.8%
112 12
 
3.2%
102 12
 
3.2%
106 12
 
3.2%
119 12
 
3.2%
98 12
 
3.2%
80 12
 
3.2%
94 11
 
3.0%
115 11
 
3.0%
120 11
 
3.0%
Other values (46) 253
68.0%
ValueCountFrequency (%)
80 12
3.2%
81 3
 
0.8%
83 3
 
0.8%
84 4
 
1.1%
85 3
 
0.8%
87 6
1.6%
88 1
 
0.3%
89 3
 
0.8%
90 5
1.3%
91 4
 
1.1%
ValueCountFrequency (%)
140 10
2.7%
138 1
 
0.3%
137 1
 
0.3%
135 1
 
0.3%
134 5
1.3%
133 3
 
0.8%
132 2
 
0.5%
131 2
 
0.5%
130 5
1.3%
129 6
1.6%

height_in_cm
Real number (ℝ)

Distinct50
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.14785
Minimum145
Maximum203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-09-21T02:39:45.215608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum145
5-th percentile157.55
Q1168.75
median176
Q3181.25
95-th percentile190.45
Maximum203
Range58
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation9.8088013
Coefficient of variation (CV)0.056002979
Kurtosis0.1402427
Mean175.14785
Median Absolute Deviation (MAD)6
Skewness-0.093103794
Sum65155
Variance96.212583
MonotonicityNot monotonic
2023-09-21T02:39:45.532828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177 21
 
5.6%
180 18
 
4.8%
176 17
 
4.6%
174 17
 
4.6%
175 17
 
4.6%
182 16
 
4.3%
178 15
 
4.0%
170 14
 
3.8%
171 13
 
3.5%
167 13
 
3.5%
Other values (40) 211
56.7%
ValueCountFrequency (%)
145 1
 
0.3%
148 2
 
0.5%
152 1
 
0.3%
153 1
 
0.3%
154 1
 
0.3%
155 4
1.1%
156 3
0.8%
157 6
1.6%
158 1
 
0.3%
159 5
1.3%
ValueCountFrequency (%)
203 1
 
0.3%
202 1
 
0.3%
199 3
0.8%
197 2
 
0.5%
196 2
 
0.5%
195 2
 
0.5%
193 1
 
0.3%
192 3
0.8%
191 4
1.1%
190 6
1.6%

weight_in_kg
Real number (ℝ)

Distinct65
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.873656
Minimum40
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-09-21T02:39:45.954132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile45
Q159
median68
Q379
95-th percentile93.45
Maximum121
Range81
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.840306
Coefficient of variation (CV)0.21547144
Kurtosis0.045379897
Mean68.873656
Median Absolute Deviation (MAD)10
Skewness0.30049731
Sum25621
Variance220.23467
MonotonicityNot monotonic
2023-09-21T02:39:46.524574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 15
 
4.0%
64 14
 
3.8%
77 12
 
3.2%
61 12
 
3.2%
62 11
 
3.0%
72 11
 
3.0%
67 11
 
3.0%
79 10
 
2.7%
73 10
 
2.7%
63 9
 
2.4%
Other values (55) 257
69.1%
ValueCountFrequency (%)
40 9
2.4%
41 1
 
0.3%
42 3
 
0.8%
43 3
 
0.8%
44 1
 
0.3%
45 4
1.1%
46 2
 
0.5%
47 3
 
0.8%
48 6
1.6%
49 5
1.3%
ValueCountFrequency (%)
121 1
 
0.3%
117 1
 
0.3%
116 1
 
0.3%
106 1
 
0.3%
103 1
 
0.3%
102 1
 
0.3%
98 3
0.8%
97 2
0.5%
96 1
 
0.3%
95 3
0.8%

payment_due
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct372
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.09607
Minimum0.38914482
Maximum498.65548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-09-21T02:39:47.089827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.38914482
5-th percentile21.070523
Q1118.13867
median223.06945
Q3364.37596
95-th percentile459.6294
Maximum498.65548
Range498.26634
Interquartile range (IQR)246.23729

Descriptive statistics

Standard deviation141.75128
Coefficient of variation (CV)0.6003966
Kurtosis-1.2132466
Mean236.09607
Median Absolute Deviation (MAD)125.23134
Skewness0.08473766
Sum87827.738
Variance20093.424
MonotonicityNot monotonic
2023-09-21T02:39:47.651471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
398.8645542 1
 
0.3%
420.7554201 1
 
0.3%
325.5208658 1
 
0.3%
87.00095261 1
 
0.3%
412.0131909 1
 
0.3%
374.728007 1
 
0.3%
182.9064334 1
 
0.3%
127.594015 1
 
0.3%
222.7036814 1
 
0.3%
50.14507542 1
 
0.3%
Other values (362) 362
97.3%
ValueCountFrequency (%)
0.3891448186 1
0.3%
1.319490218 1
0.3%
3.189995928 1
0.3%
3.687843809 1
0.3%
4.351235352 1
0.3%
4.474588272 1
0.3%
6.340851865 1
0.3%
8.071181933 1
0.3%
8.699954781 1
0.3%
12.34008468 1
0.3%
ValueCountFrequency (%)
498.6554824 1
0.3%
497.6031973 1
0.3%
497.526513 1
0.3%
495.4944651 1
0.3%
495.21366 1
0.3%
489.8539183 1
0.3%
489.3164132 1
0.3%
488.0457528 1
0.3%
487.809257 1
0.3%
485.2359077 1
0.3%

last_visit_in_days_ago
Real number (ℝ)

Distinct51
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.79301
Minimum156
Maximum211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-09-21T02:39:48.227840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum156
5-th percentile166
Q1176.75
median183
Q3189
95-th percentile198
Maximum211
Range55
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation9.8145969
Coefficient of variation (CV)0.053692408
Kurtosis0.054142248
Mean182.79301
Median Absolute Deviation (MAD)6
Skewness-0.10224224
Sum67999
Variance96.326312
MonotonicityNot monotonic
2023-09-21T02:39:48.688879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187 21
 
5.6%
185 21
 
5.6%
182 17
 
4.6%
180 16
 
4.3%
181 15
 
4.0%
175 15
 
4.0%
183 14
 
3.8%
190 14
 
3.8%
184 14
 
3.8%
178 14
 
3.8%
Other values (41) 211
56.7%
ValueCountFrequency (%)
156 2
0.5%
157 2
0.5%
160 2
0.5%
161 3
0.8%
162 2
0.5%
163 2
0.5%
164 1
 
0.3%
165 2
0.5%
166 4
1.1%
167 3
0.8%
ValueCountFrequency (%)
211 1
 
0.3%
208 2
 
0.5%
207 1
 
0.3%
206 1
 
0.3%
205 1
 
0.3%
204 1
 
0.3%
202 1
 
0.3%
201 1
 
0.3%
200 5
1.3%
199 4
1.1%

visit_duration_in_mins
Real number (ℝ)

Distinct109
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.782258
Minimum10
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-09-21T02:39:49.192701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile16
Q139
median64.5
Q391
95-th percentile114
Maximum120
Range110
Interquartile range (IQR)52

Descriptive statistics

Standard deviation31.579761
Coefficient of variation (CV)0.48747546
Kurtosis-1.1861425
Mean64.782258
Median Absolute Deviation (MAD)26.5
Skewness-0.0089511652
Sum24099
Variance997.2813
MonotonicityNot monotonic
2023-09-21T02:39:49.500381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 10
 
2.7%
19 9
 
2.4%
48 8
 
2.2%
101 7
 
1.9%
84 6
 
1.6%
82 6
 
1.6%
40 6
 
1.6%
26 6
 
1.6%
91 6
 
1.6%
60 6
 
1.6%
Other values (99) 302
81.2%
ValueCountFrequency (%)
10 4
1.1%
11 3
 
0.8%
12 1
 
0.3%
13 2
 
0.5%
14 4
1.1%
15 4
1.1%
16 3
 
0.8%
17 2
 
0.5%
18 2
 
0.5%
19 9
2.4%
ValueCountFrequency (%)
120 1
 
0.3%
119 4
1.1%
118 3
0.8%
117 2
0.5%
116 4
1.1%
115 3
0.8%
114 3
0.8%
113 3
0.8%
112 3
0.8%
111 4
1.1%

number_of_tests
Real number (ℝ)

Distinct9
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.905914
Minimum0
Maximum8
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-09-21T02:39:49.769824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4827917
Coefficient of variation (CV)0.37962732
Kurtosis-0.40204417
Mean3.905914
Median Absolute Deviation (MAD)1
Skewness0.13262424
Sum1453
Variance2.1986711
MonotonicityNot monotonic
2023-09-21T02:39:50.037642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 93
25.0%
4 91
24.5%
5 63
16.9%
2 52
14.0%
6 46
12.4%
7 14
 
3.8%
1 9
 
2.4%
0 3
 
0.8%
8 1
 
0.3%
ValueCountFrequency (%)
0 3
 
0.8%
1 9
 
2.4%
2 52
14.0%
3 93
25.0%
4 91
24.5%
5 63
16.9%
6 46
12.4%
7 14
 
3.8%
8 1
 
0.3%
ValueCountFrequency (%)
8 1
 
0.3%
7 14
 
3.8%
6 46
12.4%
5 63
16.9%
4 91
24.5%
3 93
25.0%
2 52
14.0%
1 9
 
2.4%
0 3
 
0.8%

prescription_cost
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct372
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.27917
Minimum0.24991804
Maximum149.79339
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-09-21T02:39:50.316924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.24991804
5-th percentile7.8828516
Q139.326767
median75.10454
Q3113.05378
95-th percentile144.39906
Maximum149.79339
Range149.54347
Interquartile range (IQR)73.727011

Descriptive statistics

Standard deviation43.450962
Coefficient of variation (CV)0.57719768
Kurtosis-1.1666976
Mean75.27917
Median Absolute Deviation (MAD)35.928194
Skewness0.075936654
Sum28003.851
Variance1887.9861
MonotonicityNot monotonic
2023-09-21T02:39:50.626118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108.4286367 1
 
0.3%
65.16710059 1
 
0.3%
142.2302585 1
 
0.3%
148.5703214 1
 
0.3%
148.1884066 1
 
0.3%
145.4074398 1
 
0.3%
130.3115099 1
 
0.3%
139.151571 1
 
0.3%
133.9764729 1
 
0.3%
85.95884401 1
 
0.3%
Other values (362) 362
97.3%
ValueCountFrequency (%)
0.2499180382 1
0.3%
0.9410101173 1
0.3%
1.202730798 1
0.3%
1.581152692 1
0.3%
2.206510214 1
0.3%
2.81136417 1
0.3%
3.011295125 1
0.3%
3.166317014 1
0.3%
3.484386493 1
0.3%
3.720028316 1
0.3%
ValueCountFrequency (%)
149.7933882 1
0.3%
149.7512547 1
0.3%
149.3492572 1
0.3%
149.3148247 1
0.3%
149.1745741 1
0.3%
148.5703214 1
0.3%
148.3228537 1
0.3%
148.2026554 1
0.3%
148.1884066 1
0.3%
148.0561967 1
0.3%

Interactions

2023-09-21T02:39:06.928977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:36:57.708375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:12.801066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:24.122779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:35.689854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:46.638880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:59.336360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:30.674868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:42.394074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:57.414330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:09.853770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:36:58.336706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:13.360868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:24.742247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:36.300949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:47.623764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:02.600112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:31.282461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:43.196432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:57.865832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:11.879764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:36:58.766394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:13.608217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:24.974111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:36.683148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:49.039965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:05.515227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:31.698054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:43.774827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:58.117204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:13.846958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:36:59.199533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:13.859778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:25.232303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:37.081828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:49.507467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:07.553820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:32.117341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:44.397784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:58.384119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:17.531277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:02.730019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:14.338045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:25.714646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:37.643063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:50.166071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:09.776717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:32.694425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:45.156777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:58.808724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:20.202702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:03.403160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:14.931147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:26.308368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:38.312178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:50.897220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:12.029005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:33.355724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:47.236135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:59.331210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:25.660599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:06.959716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:18.054791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:29.216910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:41.372942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:53.949599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:17.575670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:36.293640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:50.550205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:02.242930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:27.780417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:07.872032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:18.573410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:29.729250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:41.937262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:54.596615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:19.727278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:36.870168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:51.335575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:02.667004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:30.303244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:09.163015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:19.779038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:30.587798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:42.848952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:55.601183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:22.992827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:37.830003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:52.593371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:03.422212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:32.201329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:09.491739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:20.148604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:30.813526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:43.179229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:37:55.972030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:24.889981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:38.162326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:38:53.349077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-21T02:39:03.589190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-21T02:39:50.904364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
patient_ageaverage_hraverage_bpheight_in_cmweight_in_kgpayment_duelast_visit_in_days_agovisit_duration_in_minsnumber_of_testsprescription_costgenderstatehas_insurancevisited_last_monthpayment_methodpreferred_doctordisease_diagnosedmedication_prescribedtype_of_appointment
patient_age1.000-0.008-0.0910.0360.0850.080-0.080-0.0440.031-0.0110.1080.0000.0000.2130.0000.0000.0000.0000.183
average_hr-0.0081.000-0.059-0.0440.0980.0180.0860.028-0.001-0.0180.0910.0000.0000.1620.0000.0130.0000.0660.000
average_bp-0.091-0.0591.000-0.0530.058-0.107-0.0090.0070.023-0.0830.0720.0160.1580.0000.0000.0520.0000.0370.062
height_in_cm0.036-0.044-0.0531.000-0.030-0.000-0.0430.0220.0210.0460.0000.1030.0000.1260.0000.0000.0000.0970.093
weight_in_kg0.0850.0980.058-0.0301.0000.0760.081-0.0490.002-0.0300.0000.0320.0000.0000.0970.0000.1140.0000.030
payment_due0.0800.018-0.107-0.0000.0761.000-0.007-0.0190.0450.0081.0001.0001.0001.0001.0001.0001.0001.0001.000
last_visit_in_days_ago-0.0800.086-0.009-0.0430.081-0.0071.0000.0090.0250.0070.1120.0000.0000.0000.1650.0000.0000.0000.107
visit_duration_in_mins-0.0440.0280.0070.022-0.049-0.0190.0091.0000.0220.0460.0920.1060.0000.1740.0000.0000.0000.1320.000
number_of_tests0.031-0.0010.0230.0210.0020.0450.0250.0221.000-0.0390.0000.0000.0000.0000.0000.0000.0550.0850.073
prescription_cost-0.011-0.018-0.0830.046-0.0300.0080.0070.046-0.0391.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
gender0.1080.0910.0720.0000.0001.0000.1120.0920.0001.0001.0000.0990.0660.0000.0000.0660.0000.0000.059
state0.0000.0000.0160.1030.0321.0000.0000.1060.0001.0000.0991.0000.0000.0000.0000.0000.0000.0000.144
has_insurance0.0000.0000.1580.0000.0001.0000.0000.0000.0001.0000.0660.0001.0000.0000.0000.0000.0000.0000.000
visited_last_month0.2130.1620.0000.1260.0001.0000.0000.1740.0001.0000.0000.0000.0001.0000.0350.0000.0000.1030.000
payment_method0.0000.0000.0000.0000.0971.0000.1650.0000.0001.0000.0000.0000.0000.0351.0000.0240.0470.0000.000
preferred_doctor0.0000.0130.0520.0000.0001.0000.0000.0000.0001.0000.0660.0000.0000.0000.0241.0000.0000.0000.000
disease_diagnosed0.0000.0000.0000.0000.1141.0000.0000.0000.0551.0000.0000.0000.0000.0000.0470.0001.0000.0000.000
medication_prescribed0.0000.0660.0370.0970.0001.0000.0000.1320.0851.0000.0000.0000.0000.1030.0000.0000.0001.0000.000
type_of_appointment0.1830.0000.0620.0930.0301.0000.1070.0000.0731.0000.0590.1440.0000.0000.0000.0000.0000.0001.000

Missing values

2023-09-21T02:39:37.852152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-21T02:39:38.531226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

patient_agegendercitystatehas_insurancevisited_last_monthpayment_methodpreferred_doctordisease_diagnosedmedication_prescribedtype_of_appointmentaverage_hraverage_bpheight_in_cmweight_in_kgpayment_duelast_visit_in_days_agovisit_duration_in_minsnumber_of_testsprescription_cost
7961418MaleNorth JulielandVermontYesNoCashDr. JohnsonNoneMed_BEmergency5710718863398.8645541757141177115108.42863666565667
3221056MaleEmmatownConnecticutNoYesCardDr. WilliamsFluMed_AEmergency7011317784411.944056126805618919578.68179417044135
5307871FemaleSouth MelissaOregonYesYesCashDr. BrownCovid-19Med_BGeneral901181489043.6117909355636718012623.316717612185627
7197660MaleHamptontownNorth DakotaYesYesCardDr. BrownColdMed_CFollow-Up6912217151282.3824263988475419832470.43744427899895
2003453MaleNorth SierraviewMarylandYesYesInsuranceDr. BrownCovid-19Med_DFollow-Up839518076447.5440119706905179116496.55864008886802
8929818FemaleNatalieviewNorth CarolinaYesNoCashDr. JonesFluMed_BFollow-Up8310216764124.9282914132332517476493.04285175289122
2557748MaleSouth DanielshireTexasNoYesCashDr. JohnsonNoneMed_CEmergency8211418465166.749600625900481671064124.45599875890643
7684218FemaleNew BrucehavenAlabamaYesYesInsuranceDr. JohnsonFluMed_AFollow-Up749617669332.261626204693217869214.630766509170389
1115572MalePort AustinOregonNoYesInsuranceDr. WilliamsColdMed_EEmergency889319040300.9705725063493618241624.859176055465877
4432249FemaleNew DanielmouthColoradoYesYesInsuranceDr. BrownCovid-19Med_DFollow-Up7810118477421.96075797553704173674148.05619672366677
patient_agegendercitystatehas_insurancevisited_last_monthpayment_methodpreferred_doctordisease_diagnosedmedication_prescribedtype_of_appointmentaverage_hraverage_bpheight_in_cmweight_in_kgpayment_duelast_visit_in_days_agovisit_duration_in_minsnumber_of_testsprescription_cost
8228649MaleBakerlandLouisianaNoYesInsuranceDr. SmithNoneMed_BFollow-Up781191749335.9805935130378961951203142.44586066018564
2681336MaleRamirezbergConnecticutNoYesInsuranceDr. BrownFluMed_BSpecialist8810117757118.99967866444588169100364.75135045734744
3736661MaleSouth MaryTexasNoYesCardDr. WilliamsCovid-19Med_AGeneral6912917558399.73410666951526177119322.586418362839872
3808445FemaleTracylandSouth DakotaNoYesCashDr. WilliamsColdMed_ASpecialist709817072164.11131191372542176110555.921570929847505
1215324FemaleEast GarrettfurtHawaiiNoYesCardDr. BrownNoneMed_CFollow-Up8213318261156.032329071352517851670.93119082573236
3925128MaleKristinportOregonNoNoCashDr. SmithFluMed_EEmergency9311418162217.5853293779934218956679.70378443819558
9253863FemaleEast StephenSouth CarolinaNoYesCashDr. BrownFluMed_EGeneral7811416886108.2135168770055317443352.504873409025976
7181973FemaleJustinboroughHawaiiYesNoInsuranceDr. SmithFluMed_EFollow-Up6812216477396.88584817653697173503135.53601342117057
897540FemaleEricbergNorth DakotaYesYesInsuranceDr. BrownFluMed_CSpecialist6811817664154.85587387284588211101699.23476699803356
2215853MaleMorenoburghSouth CarolinaNoNoCashDr. JohnsonAllergyMed_BFollow-Up801201827843.119959878894516830321.948476019001635